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Found 18 papers, 8 papers with code
Nearly Optimal Regret for Decentralized Online Convex Optimization
Previous studies have established $O(n^{5/4}\rho^{-1/2}\sqrt{T})$ and ${O}(n^{3/2}\rho^{-1}\log T)$ regret bounds for convex and strongly convex functions respectively, where $n$ is the number of local learners, $\rho<1$ is the spectral gap of the communication matrix, and $T$ is the time horizon.
EAT: Towards Long-Tailed Out-of-Distribution Detection
The main difficulty lies in distinguishing OOD data from samples belonging to the tail classes, as the ability of a classifier to detect OOD instances is not strongly correlated with its accuracy on the in-distribution classes.
Bridging the Gap: Learning Pace Synchronization for Open-World Semi-Supervised Learning
In open-world semi-supervised learning, a machine learning model is tasked with uncovering novel categories from unlabeled data while maintaining performance on seen categories from labeled data.
Novel Class Discovery
Open-World Semi-Supervised Learning
+1
Parameter-Efficient Long-Tailed Recognition
In this paper, we propose PEL, a fine-tuning method that can effectively adapt pre-trained models to long-tailed recognition tasks in fewer than 20 epochs without the need for extra data.
Ranked #1 on
Long-tail Learning
on CIFAR-100-LT (ρ=10)
(using extra training data)
Fine-Grained Image Classification
Long-tail learning with class descriptors
Scene-Aware Feature Matching
Current feature matching methods focus on point-level matching, pursuing better representation learning of individual features, but lacking further understanding of the scene.
RIS-Aided Wideband Holographic DFRC
This surface is crucial for designing compact low-cost wideband wireless systems, wherein ultra-massive antenna arrays are required to compensate for the losses incurred by severe attenuation and diffraction.
Towards Realistic Long-Tailed Semi-Supervised Learning: Consistency Is All You Need
While long-tailed semi-supervised learning (LTSSL) has received tremendous attention in many real-world classification problems, existing LTSSL algorithms typically assume that the class distributions of labeled and unlabeled data are almost identical.
Generalized Differentiable RANSAC
We propose $\nabla$-RANSAC, a generalized differentiable RANSAC that allows learning the entire randomized robust estimation pipeline.
A Survey on Extreme Multi-label Learning
Multi-label learning has attracted significant attention from both academic and industry field in recent decades.
Transfer and Share: Semi-Supervised Learning from Long-Tailed Data
TRAS transforms the imbalanced pseudo-label distribution of a traditional SSL model via a delicate function to enhance the supervisory signals for minority classes.
Adaptive Reordering Sampler with Neurally Guided MAGSAC
We propose a new sampler for robust estimators that always selects the sample with the highest probability of consisting only of inliers.
Prototypical Classifier for Robust Class-Imbalanced Learning
Deep neural networks have been shown to be very powerful methods for many supervised learning tasks.
Robust Long-Tailed Learning under Label Noise
To overcome this limitation, we establish a new prototypical noise detection method by designing a distance-based metric that is resistant to label noise.
Ranked #25 on
Image Classification
on mini WebVision 1.0
NGC: A Unified Framework for Learning with Open-World Noisy Data
The existence of noisy data is prevalent in both the training and testing phases of machine learning systems, which inevitably leads to the degradation of model performance.
Ranked #18 on
Image Classification
on mini WebVision 1.0
Sparse Array Beampattern Synthesis via Majorization-Based ADMM
Beampattern synthesis is a key problem in many wireless applications.
Improving Tail Label Prediction for Extreme Multi-label Learning
Extreme multi-label learning (XML) works to annotate objects with relevant labels from an extremely large label set.
Fuzzy Graph Neural Network for Few-Shot Learning
According to the output of edge prediction, we design a fuzzy membership function to achieve more exact relationship representations for node classification.
MixPUL: Consistency-based Augmentation for Positive and Unlabeled Learning
To facilitate supervised consistency, reliable negative examples are mined from unlabeled data due to the absence of negative samples.
